first commit

DESCRIPTION

+Package: errorgen
+Type: Package
+Version: 0.0.1
+Authors@R: c(person("Jean-Marie", "Lepioufle", , "jml@nilu.no", role=c("aut","cre")))
+Title: aqgen
+Description: Generation of error in a dataset.
+Depends:
+    R (>= 3.4.1)
+Imports:
+    stats,
+    dplyr,
+    utils
+Suggests:
+    knitr,
+VignetteBuilder: knitr
+License: GPL-3
+Encoding: UTF-8
+LazyData: false
+RoxygenNote: 6.1.1

NAMESPACE

+# Generated by roxygen2: do not edit by hand
+
+export(error_input)
+export(error_output)
+export(error_remnant)
+export(error_structural)
+export(gen_error)
+export(partition)

R/error.R

+
+#' error_input
+
+#' error_input
+#' @param x true input
+#' @param phi_f1 phi_f1
+#' @keywords aqoslogen
+#' @export
+#' @examples
+#' \dontrun{
+#' error_input()
+#' }
+#
+error_input <- function(x,phi_f1=3/100){
+    res <- x
+    n <- length(res)
+    res <- stats::rnorm(n,x,sd=abs(phi_f1)*x)
+    return(res)
+}
+
+
+#' error_output
+
+#' error_output
+#' @param y true output
+#' @param phi_g1 phi_g1
+#' @keywords aqoslogen
+#' @export
+#' @examples
+#' \dontrun{
+#' error_output()
+#' }
+#
+error_output <- function(y,phi_g1=3/100){
+    res <- y
+    n <- length(res)
+    res <- stats::rnorm(n,y,sd=abs(phi_g1)*y)
+    return(res)
+}
+
+#' error_structural
+
+#' error_structural
+#' @param m true model output
+#' @param phi_h1 phi_h1
+#' @keywords aqoslogen
+#' @export
+#' @examples
+#' \dontrun{
+#' error_structural()
+#' }
+#
+error_structural <- function(m,phi_h1=8/100){
+  n <- length(m)
+  res <- stats::rnorm(n,m,sd=abs(phi_h1)*m)
+  return(res)
+}
+
+#' error_remnant
+
+#' error_remnant
+#' @param y_hat true prediction
+#' @param beta_r beta_r
+#' @param sigma_r sigma_r
+#' @keywords aqoslogen
+#' @export
+#' @examples
+#' \dontrun{
+#' error_remnant()
+#' }
+#
+error_remnant <- function(y_hat,beta_r=0.8,sigma_r=0.5){
+  n <- length(y_hat)
+  res <- 1/beta_r*stats::rnorm(n,y_hat,sigma_r)
+  return(res)
+}

R/gen_error.R

+
+#' gen_error
+
+#' gen_error
+#' @param dataset data.frame
+#' @param target column name of the target
+#' @param predictors column names of the predictors
+#' @param FUN_fit function for the fitting
+#' @param FUN_predict function for the prediciton
+#' @param error_type chosen error type 'r','s','sr','or','sor','isor'
+#' @param par parameters of the input, output, structure, and remnant errors
+#' @keywords aqoslogen
+#' @export
+#' @examples
+#' \dontrun{
+#' gen_error()
+#' }
+#
+gen_error <- function(dataset,target,predictors,FUN_fit,FUN_predict,error_type=c("r","or","sor","isor"),par=list(
+      phi_f1 = list(mean=5.64/1.96,sd=10),
+      phi_g1 = list(mean=5.64/1.96,sd=10),
+      phi_h1 = list(mean=0.1,sd=0.1),
+      beta_r  = list(mean=1,sd=2),
+      sig_r  = list(df=1,scale=0.2))) {
+
+  res <- switch(error_type,
+
+  "r"    = gen_error_r(dataset,target,predictors,FUN_fit,FUN_predict,par[["beta_r"]],par[["sig_r"]]),
+  "s"    = gen_error_r(dataset,target,predictors,FUN_fit,FUN_predict,par[["phi_h1"]]),
+  "sr"   = gen_error_sr(dataset,target,predictors,FUN_fit,FUN_predict,par[["phi_h1"]],par[["beta_r"]],par[["sig_r"]]),
+  "or"   = gen_error_or(dataset,target,predictors,FUN_fit,FUN_predict,par[["phi_g1"]],par[["beta_r"]],par[["sig_r"]]),
+  "sor"  = gen_error_sor(dataset,target,predictors,FUN_fit,FUN_predict,par[["phi_h1"]],par[["phi_g1"]],par[["beta_r"]],par[["sig_r"]]),
+  "isor" = gen_error_isor(dataset,target,predictors,FUN_fit,FUN_predict,par[["phi_f1"]],par[["phi_h1"]],par[["phi_g1"]],par[["beta_r"]],par[["sig_r"]]),
+  stop("error_type not included yet"))
+
+  return(res)
+
+}
+
+
+# x,y,m perfect, with remant
+gen_error_r <- function(dataset,target,predictors,FUN_fit,FUN_predict,beta_r,sig_r){
+  split_dataset <- partition(dataset)
+  tmp_model <- FUN_fit(split_dataset$train,target=target,predictors=predictors)
+  tmp_pred <- error_remnant(FUN_predict(tmp_model,split_dataset$test),beta_r,sig_r)
+  dataset <- dplyr::bind_cols(split_dataset$test,prediction=tmp_pred)
+  return(dataset)
+}
+
+# x,y, perfect, m non-perfect, without remnant
+gen_error_s <- function(dataset,target,predictors,FUN_fit,FUN_predict,phi_h1){
+  split_dataset <- partition(dataset)
+  tmp_model <- FUN_fit(split_dataset$train,target=target,predictors=predictors)
+  tmp_pred <- error_structural(FUN_predict(tmp_model,split_dataset$test),phi_h1)
+  dataset <- dplyr::bind_cols(split_dataset$test,prediction=tmp_pred)
+  return(dataset)
+}
+
+# x,y, perfect, m non-perfect, with remnant
+gen_error_sr <- function(dataset,target,predictors,FUN_fit,FUN_predict,phi_h1,beta_r,sig_r){
+  split_dataset <- partition(dataset)
+  tmp_model <- FUN_fit(split_dataset$train,target=target,predictors=predictors)
+  tmp_pred <- error_structural(error_remnant(FUN_predict(tmp_model,split_dataset$test),beta_r,sig_r),phi_h1)
+  dataset <- dplyr::bind_cols(split_dataset$test,prediction=tmp_pred)
+  return(dataset)
+}
+
+# x, m perfect, y non-perfect, with remnant
+gen_error_or <- function(dataset,target,predictors,FUN_fit,FUN_predict,phi_g1,beta_r,sig_r){
+  dataset[[target]] <- error_output(dataset[[target]],phi_g1)
+  split_dataset <- partition(dataset)
+  tmp_model <- FUN_fit(split_dataset$train,target=target,predictors=predictors)
+  tmp_pred <- error_remnant(FUN_predict(tmp_model,split_dataset$test),beta_r,sig_r)
+  dataset <- dplyr::bind_cols(split_dataset$test,prediction=tmp_pred)
+  return(dataset)
+}
+
+# x,perfect, y and m non-perfect, with remnant
+gen_error_sor <- function(dataset,target,predictors,FUN_fit,FUN_predict,phi_h1,phi_g1,beta_r,sig_r){
+
+  dataset[[target]] <- error_output(dataset[[target]],phi_g1)
+  split_dataset <- partition(dataset)
+  tmp_model <- FUN_fit(split_dataset$train,target=target,predictors=predictors)
+  tmp_pred <- error_structural(error_remnant(FUN_predict(tmp_model,split_dataset$test),beta_r,sig_r),phi_h1)
+  dataset <- dplyr::bind_cols(split_dataset$test,prediction=tmp_pred)
+  return(dataset)
+}
+
+# x,y, m non-perfect, with remnant
+gen_error_isor <- function(dataset,target,predictors,FUN_fit,FUN_predict,phi_f1,phi_h1,phi_g1,beta_r,sig_r){
+
+  lapply(predictors, function(x){
+     dataset[[x]] <<- error_input(dataset[[x]],phi_f1)
+  })
+  dataset[[target]] <- error_output(dataset[[target]],phi_g1)
+  split_dataset <- partition(dataset)
+  tmp_model <- FUN_fit(split_dataset$train,target=target,predictors=predictors)
+  tmp_pred <- error_structural(error_remnant(FUN_predict(tmp_model,split_dataset$test),beta_r,sig_r),phi_h1)
+  dataset <- dplyr::bind_cols(split_dataset$test,prediction=tmp_pred)
+  return(dataset)
+}

R/utils.R

+#' partition
+
+#' partition
+#' @param dataset list or tibble
+#' @param train ratio for training
+#' @param test ratio for testing
+#' @param seed seed
+#' @keywords errorgen
+#' @export
+#' @examples
+#' \dontrun{
+#' partition()
+#' }
+#
+partition <- function(dataset,train=0.8,test=0.2,seed=NULL){
+  if (!is.null(seed)) set.seed(seed)
+
+  fact <- sample(c("train","test"),dim(dataset)[1],prob=c(train,test),replace=TRUE)
+  res <- split(dataset,fact)
+  return(res)
+}

man/error_input.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/error.R
+\name{error_input}
+\alias{error_input}
+\title{error_input
+error_input}
+\usage{
+error_input(x, phi_f1 = 3/100)
+}
+\arguments{
+\item{x}{true input}
+
+\item{phi_f1}{phi_f1}
+}
+\description{
+error_input
+error_input
+}
+\examples{
+\dontrun{
+error_input()
+}
+}
+\keyword{aqoslogen}

man/error_output.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/error.R
+\name{error_output}
+\alias{error_output}
+\title{error_output
+error_output}
+\usage{
+error_output(y, phi_g1 = 3/100)
+}
+\arguments{
+\item{y}{true output}
+
+\item{phi_g1}{phi_g1}
+}
+\description{
+error_output
+error_output
+}
+\examples{
+\dontrun{
+error_output()
+}
+}
+\keyword{aqoslogen}

man/error_remnant.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/error.R
+\name{error_remnant}
+\alias{error_remnant}
+\title{error_remnant
+error_remnant}
+\usage{
+error_remnant(y_hat, beta_r = 0.8, sigma_r = 0.5)
+}
+\arguments{
+\item{y_hat}{true prediction}
+
+\item{beta_r}{beta_r}
+
+\item{sigma_r}{sigma_r}
+}
+\description{
+error_remnant
+error_remnant
+}
+\examples{
+\dontrun{
+error_remnant()
+}
+}
+\keyword{aqoslogen}

man/error_structural.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/error.R
+\name{error_structural}
+\alias{error_structural}
+\title{error_structural
+error_structural}
+\usage{
+error_structural(m, phi_h1 = 8/100)
+}
+\arguments{
+\item{m}{true model output}
+
+\item{phi_h1}{phi_h1}
+}
+\description{
+error_structural
+error_structural
+}
+\examples{
+\dontrun{
+error_structural()
+}
+}
+\keyword{aqoslogen}

man/gen_error.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen_error.R
+\name{gen_error}
+\alias{gen_error}
+\title{gen_error
+gen_error}
+\usage{
+gen_error(dataset, target, predictors, FUN_fit, FUN_predict,
+  error_type = c("r", "or", "sor", "isor"), par = list(phi_f1 =
+  list(mean = 5.64/1.96, sd = 10), phi_g1 = list(mean = 5.64/1.96, sd =
+  10), phi_h1 = list(mean = 0.1, sd = 0.1), beta_r = list(mean = 1, sd =
+  2), sig_r = list(df = 1, scale = 0.2)))
+}
+\arguments{
+\item{dataset}{data.frame}
+
+\item{target}{column name of the target}
+
+\item{predictors}{column names of the predictors}
+
+\item{FUN_fit}{function for the fitting}
+
+\item{FUN_predict}{function for the prediciton}
+
+\item{error_type}{chosen error type 'r','s','sr','or','sor','isor'}
+
+\item{par}{parameters of the input, output, structure, and remnant errors}
+}
+\description{
+gen_error
+gen_error
+}
+\examples{
+\dontrun{
+gen_error()
+}
+}
+\keyword{aqoslogen}

man/partition.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{partition}
+\alias{partition}
+\title{partition
+partition}
+\usage{
+partition(dataset, train = 0.8, test = 0.2, seed = NULL)
+}
+\arguments{
+\item{dataset}{list or tibble}
+
+\item{train}{ratio for training}
+
+\item{test}{ratio for testing}
+
+\item{seed}{seed}
+}
+\description{
+partition
+partition
+}
+\examples{
+\dontrun{
+partition()
+}
+}
+\keyword{errorgen}